code: def forward(self, pvam_feature, gsrm_feature):
      b, t, c1 = pvam_feature.shape
      b, t, c2 = gsrm_feature.shape
      combine_feature_ = paddle.concat(
          [pvam_feature, gsrm_feature], axis=2
      )
      img_comb_feature_ = paddle.reshape(
          combine_feature_, shape=[-1, c1 + c2]
      )
      img_comb_feature_map = self.fc0(img_comb_feature_)
      img_comb_feature_map = F.sigmoid(img_comb_feature_map)
      img_comb_feature_map = paddle.reshape(
          img_comb_feature_map, shape=[-1, t, c1]
      )
      combine_feature = (
          img_comb_feature_map * pvam_feature
          + (1.0 - img_comb_feature_map) * gsrm_feature
      )
      img_comb_feature = paddle.reshape(combine_feature, shape=[-1, c1])
  
      out = self.fc1(img_comb_feature)
      return out
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import nn, ParamAttr
  from paddle.nn import functional
  import numpy
  from .self_attention import WrapEncoderForFeature
  from .self_attention import WrapEncoder
  from paddle.static import Program
  from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling the VSFD obj's underlying member method code with example usage: obj.forward(pvam_feature, gsrm_feature) and returns: out